# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2020 MLBenchmark Group. All rights reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Create masked LM/next sentence masked_lm TF examples for BERT."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import random
import tokenization_local
import tensorflow as tf

import h5py
import numpy as np

hdf5_compression_method = None

# flags = tf.flags
flags = tf.compat.v1.flags

FLAGS = flags.FLAGS

flags.DEFINE_string(
    "input_file", None, "Input raw text file (or comma-separated list of files)."
)

flags.DEFINE_string(
    "output_file", None, "Output TF example file (or comma-separated list of files)."
)

flags.DEFINE_string(
    "vocab_file", None, "The vocabulary file that the BERT model was trained on."
)

flags.DEFINE_bool(
    "do_lower_case",
    True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.",
)

flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")

flags.DEFINE_integer(
    "max_predictions_per_seq",
    20,
    "Maximum number of masked LM predictions per sequence.",
)

flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")

flags.DEFINE_integer(
    "dupe_factor",
    10,
    "Number of times to duplicate the input data (with different masks).",
)

flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")

flags.DEFINE_float(
    "short_seq_prob",
    0.1,
    "Probability of creating sequences which are shorter than the " "maximum length.",
)


class TrainingInstance(object):
    """A single training instance (sentence pair)."""

    def __init__(
        self, tokens, segment_ids, masked_lm_positions, masked_lm_labels, is_random_next
    ):
        self.tokens = tokens
        self.segment_ids = segment_ids
        self.is_random_next = is_random_next
        self.masked_lm_positions = masked_lm_positions
        self.masked_lm_labels = masked_lm_labels

    def __str__(self):
        s = ""
        s += "tokens: %s\n" % (
            " ".join([tokenization_local.printable_text(x) for x in self.tokens])
        )
        s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
        s += "is_random_next: %s\n" % self.is_random_next
        s += "masked_lm_positions: %s\n" % (
            " ".join([str(x) for x in self.masked_lm_positions])
        )
        s += "masked_lm_labels: %s\n" % (
            " ".join(
                [tokenization_local.printable_text(x) for x in self.masked_lm_labels]
            )
        )
        s += "\n"
        return s

    def __repr__(self):
        return self.__str__()


def write_instance_to_example_files(
    instances, tokenizer, max_seq_length, max_predictions_per_seq, output_files
):
    """Create TF example files from `TrainingInstance`s."""
    writers = []
    h5_writers = []

    expected_instances_per_file = (
        len(instances) // len(output_files) + 500
    )  # Over-allocation to avoid resizing
    for output_file in output_files:
        h5_writers.append(
            {
                "handle": h5py.File(output_file + ".hdf5", "w"),
                "input_ids": np.zeros(
                    [expected_instances_per_file, max_seq_length], dtype="int32"
                ),
                "input_mask": np.zeros(
                    [expected_instances_per_file, max_seq_length], dtype="int32"
                ),
                "segment_ids": np.zeros(
                    [expected_instances_per_file, max_seq_length], dtype="int32"
                ),
                "masked_lm_positions": np.zeros(
                    [expected_instances_per_file, max_predictions_per_seq],
                    dtype="int32",
                ),
                "masked_lm_ids": np.zeros(
                    [expected_instances_per_file, max_predictions_per_seq],
                    dtype="int32",
                ),
                "next_sentence_labels": np.zeros(
                    expected_instances_per_file, dtype="int32"
                ),
                "len": 0,
            }
        )

    writer_index = 0

    total_written = 0

    features_h5 = collections.OrderedDict()

    for inst_index, instance in enumerate(instances):
        input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
        input_mask = [1] * len(input_ids)
        segment_ids = list(instance.segment_ids)
        assert len(input_ids) <= max_seq_length

        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        masked_lm_positions = list(instance.masked_lm_positions)
        masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
        masked_lm_weights = [1.0] * len(masked_lm_ids)

        while len(masked_lm_positions) < max_predictions_per_seq:
            masked_lm_positions.append(0)
            masked_lm_ids.append(0)
            masked_lm_weights.append(0.0)

        next_sentence_label = 1 if instance.is_random_next else 0

        h5_writers[writer_index]["input_ids"][inst_index] = input_ids
        h5_writers[writer_index]["input_mask"][inst_index] = input_mask
        h5_writers[writer_index]["segment_ids"][inst_index] = segment_ids
        h5_writers[writer_index]["masked_lm_positions"][
            inst_index
        ] = masked_lm_positions
        h5_writers[writer_index]["masked_lm_ids"][inst_index] = masked_lm_ids
        h5_writers[writer_index]["next_sentence_labels"][
            inst_index
        ] = next_sentence_label
        h5_writers[writer_index]["len"] += 1

        writer_index = (writer_index + 1) % len(h5_writers)

        total_written += 1

        if inst_index < 20:
            tf.compat.v1.logging.info("*** Example ***")
            tf.compat.v1.logging.info(
                "tokens: %s"
                % " ".join(
                    [tokenization_local.printable_text(x) for x in instance.tokens]
                )
            )

    print("saving data")
    for h5_writer in h5_writers:
        my_size = h5_writer["len"]
        h5_writer["handle"].create_dataset(
            "input_ids",
            data=h5_writer["input_ids"][:my_size],
            dtype="i4",
            compression=hdf5_compression_method,
        )
        h5_writer["handle"].create_dataset(
            "input_mask",
            data=h5_writer["input_mask"][:my_size],
            dtype="i1",
            compression=hdf5_compression_method,
        )
        h5_writer["handle"].create_dataset(
            "segment_ids",
            data=h5_writer["segment_ids"][:my_size],
            dtype="i1",
            compression=hdf5_compression_method,
        )
        h5_writer["handle"].create_dataset(
            "masked_lm_positions",
            data=h5_writer["masked_lm_positions"][:my_size],
            dtype="i4",
            compression=hdf5_compression_method,
        )
        h5_writer["handle"].create_dataset(
            "masked_lm_ids",
            data=h5_writer["masked_lm_ids"][:my_size],
            dtype="i4",
            compression=hdf5_compression_method,
        )
        h5_writer["handle"].create_dataset(
            "next_sentence_labels",
            data=h5_writer["next_sentence_labels"][:my_size],
            dtype="i1",
            compression=hdf5_compression_method,
        )
        h5_writer["handle"].flush()
        h5_writer["handle"].close()

    tf.compat.v1.logging.info("Wrote %d total instances", total_written)


def create_int_feature(values):
    feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
    return feature


def create_float_feature(values):
    feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
    return feature


def create_training_instances(
    input_files,
    tokenizer,
    max_seq_length,
    dupe_factor,
    short_seq_prob,
    masked_lm_prob,
    max_predictions_per_seq,
    rng,
):
    """Create `TrainingInstance`s from raw text."""
    all_documents = [[]]

    # Input file format:
    # (1) One sentence per line. These should ideally be actual sentences, not
    # entire paragraphs or arbitrary spans of text. (Because we use the
    # sentence boundaries for the "next sentence prediction" task).
    # (2) Blank lines between documents. Document boundaries are needed so
    # that the "next sentence prediction" task doesn't span between documents.
    for input_file in input_files:
        with tf.compat.v1.gfile.GFile(input_file, "r") as reader:
            while True:
                line = tokenization_local.convert_to_unicode(reader.readline())
                if not line:
                    break
                line = line.strip()

                # Empty lines are used as document delimiters
                if not line:
                    all_documents.append([])
                tokens = tokenizer.tokenize(line)
                if tokens:
                    all_documents[-1].append(tokens)

    # Remove empty documents
    all_documents = [x for x in all_documents if x]
    rng.shuffle(all_documents)

    vocab_words = list(tokenizer.vocab.keys())
    instances = []
    for _ in range(dupe_factor):
        for document_index in range(len(all_documents)):
            instances.extend(
                create_instances_from_document(
                    all_documents,
                    document_index,
                    max_seq_length,
                    short_seq_prob,
                    masked_lm_prob,
                    max_predictions_per_seq,
                    vocab_words,
                    rng,
                )
            )

    rng.shuffle(instances)
    return instances


def create_instances_from_document(
    all_documents,
    document_index,
    max_seq_length,
    short_seq_prob,
    masked_lm_prob,
    max_predictions_per_seq,
    vocab_words,
    rng,
):
    """Creates `TrainingInstance`s for a single document."""
    document = all_documents[document_index]

    # Account for [CLS], [SEP], [SEP]
    max_num_tokens = max_seq_length - 3

    # We *usually* want to fill up the entire sequence since we are padding
    # to `max_seq_length` anyways, so short sequences are generally wasted
    # computation. However, we *sometimes*
    # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
    # sequences to minimize the mismatch between pre-training and fine-tuning.
    # The `target_seq_length` is just a rough target however, whereas
    # `max_seq_length` is a hard limit.
    target_seq_length = max_num_tokens
    if rng.random() < short_seq_prob:
        target_seq_length = rng.randint(2, max_num_tokens)

    # We DON'T just concatenate all of the tokens from a document into a long
    # sequence and choose an arbitrary split point because this would make the
    # next sentence prediction task too easy. Instead, we split the input into
    # segments "A" and "B" based on the actual "sentences" provided by the user
    # input.
    instances = []
    current_chunk = []
    current_length = 0
    i = 0
    while i < len(document):
        segment = document[i]
        current_chunk.append(segment)
        current_length += len(segment)
        if i == len(document) - 1 or current_length >= target_seq_length:
            if current_chunk:
                # `a_end` is how many segments from `current_chunk` go into the `A`
                # (first) sentence.
                a_end = 1
                if len(current_chunk) >= 2:
                    a_end = rng.randint(1, len(current_chunk) - 1)

                tokens_a = []
                for j in range(a_end):
                    tokens_a.extend(current_chunk[j])

                tokens_b = []
                # Random next
                is_random_next = False
                if len(current_chunk) == 1 or rng.random() < 0.5:
                    is_random_next = True
                    target_b_length = target_seq_length - len(tokens_a)

                    # This should rarely go for more than one iteration for large
                    # corpora. However, just to be careful, we try to make sure that
                    # the random document is not the same as the document
                    # we're processing.
                    for _ in range(10):
                        random_document_index = rng.randint(0, len(all_documents) - 1)
                        if random_document_index != document_index:
                            break

                    random_document = all_documents[random_document_index]
                    random_start = rng.randint(0, len(random_document) - 1)
                    for j in range(random_start, len(random_document)):
                        tokens_b.extend(random_document[j])
                        if len(tokens_b) >= target_b_length:
                            break
                    # We didn't actually use these segments so we "put them back" so
                    # they don't go to waste.
                    num_unused_segments = len(current_chunk) - a_end
                    i -= num_unused_segments
                # Actual next
                else:
                    is_random_next = False
                    for j in range(a_end, len(current_chunk)):
                        tokens_b.extend(current_chunk[j])
                truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)

                assert len(tokens_a) >= 1
                assert len(tokens_b) >= 1

                tokens = []
                segment_ids = []
                tokens.append("[CLS]")
                segment_ids.append(0)
                for token in tokens_a:
                    tokens.append(token)
                    segment_ids.append(0)

                tokens.append("[SEP]")
                segment_ids.append(0)

                for token in tokens_b:
                    tokens.append(token)
                    segment_ids.append(1)
                tokens.append("[SEP]")
                segment_ids.append(1)

                (
                    tokens,
                    masked_lm_positions,
                    masked_lm_labels,
                ) = create_masked_lm_predictions(
                    tokens,
                    masked_lm_prob,
                    max_predictions_per_seq,
                    vocab_words,
                    rng,
                )
                instance = TrainingInstance(
                    tokens=tokens,
                    segment_ids=segment_ids,
                    is_random_next=is_random_next,
                    masked_lm_positions=masked_lm_positions,
                    masked_lm_labels=masked_lm_labels,
                )
                instances.append(instance)
            current_chunk = []
            current_length = 0
        i += 1

    return instances


MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"])


def create_masked_lm_predictions(
    tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng
):
    """Creates the predictions for the masked LM objective."""

    cand_indexes = []
    for i, token in enumerate(tokens):
        if token == "[CLS]" or token == "[SEP]":
            continue
        cand_indexes.append(i)

    rng.shuffle(cand_indexes)

    output_tokens = list(tokens)

    num_to_predict = min(
        max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob)))
    )

    masked_lms = []
    covered_indexes = set()
    for index in cand_indexes:
        if len(masked_lms) >= num_to_predict:
            break
        if index in covered_indexes:
            continue
        covered_indexes.add(index)

        masked_token = None
        # 80% of the time, replace with [MASK]
        if rng.random() < 0.8:
            masked_token = "[MASK]"
        else:
            # 10% of the time, keep original
            if rng.random() < 0.5:
                masked_token = tokens[index]
            # 10% of the time, replace with random word
            else:
                masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]

        output_tokens[index] = masked_token

        masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))

    masked_lms = sorted(masked_lms, key=lambda x: x.index)

    masked_lm_positions = []
    masked_lm_labels = []
    for p in masked_lms:
        masked_lm_positions.append(p.index)
        masked_lm_labels.append(p.label)

    return (output_tokens, masked_lm_positions, masked_lm_labels)


def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
    """Truncates a pair of sequences to a maximum sequence length."""
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_num_tokens:
            break

        trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
        assert len(trunc_tokens) >= 1

        # We want to sometimes truncate from the front and sometimes from the
        # back to add more randomness and avoid biases.
        if rng.random() < 0.5:
            del trunc_tokens[0]
        else:
            trunc_tokens.pop()


def main(_):
    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)

    tokenizer = tokenization_local.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case
    )

    input_files = []
    for input_pattern in FLAGS.input_file.split(","):
        input_files.extend(tf.compat.v1.gfile.Glob(input_pattern))

    tf.compat.v1.logging.info("*** Reading from input files ***")
    for input_file in input_files:
        tf.compat.v1.logging.info("  %s", input_file)

    rng = random.Random(FLAGS.random_seed)
    instances = create_training_instances(
        input_files,
        tokenizer,
        FLAGS.max_seq_length,
        FLAGS.dupe_factor,
        FLAGS.short_seq_prob,
        FLAGS.masked_lm_prob,
        FLAGS.max_predictions_per_seq,
        rng,
    )

    output_files = FLAGS.output_file.split(",")
    tf.compat.v1.logging.info("*** Writing to output files ***")
    for output_file in output_files:
        tf.compat.v1.logging.info("  %s", output_file)

    write_instance_to_example_files(
        instances,
        tokenizer,
        FLAGS.max_seq_length,
        FLAGS.max_predictions_per_seq,
        output_files,
    )


if __name__ == "__main__":
    flags.mark_flag_as_required("input_file")
    flags.mark_flag_as_required("output_file")
    flags.mark_flag_as_required("vocab_file")
    tf.compat.v1.app.run()
